The article considers lying as an epistemic attitude, analyzes its structural and substantive characteristics. In particular, it considers the pragmatic consequences of using false beliefs, primarily in scientific practice. It is shown that in a number of cases false epistemic attitudes can play a positive role, mainly as temporary hypothetical or counterfactual assumptions, as well as positive illusions. The paper also analyzes two alternative approaches towards false beliefs: “knowledge-first epistemology” of T. Williamson and “action-first epistemology” in line with epistemic pragmatism. (...) It considers the potential of the latter approach and, in particular, the way of reconciling two seemingly conflicting normative frameworks – for beliefs and for actions. This, in turn, opens the way for building a complex normative system with different “directions of fit” between beliefs and actions on one side and the world on the other. (shrink)

In the last decades, experimental philosophers have introduced the notion that conceptual analysis could use empirical evidence to back some of its claims. This opens up the possibility for the development of a corpus-based conceptual analysis. However, progress in this direction is contingent on the development of a proper account of concepts and corpus-based conceptual analysis itself that can be leveraged on textual data. In this essay, I address this problem through the question of similarity: how do we evaluate similarity (...) between two concepts, as similarity relates to identity? After a survey of prominent conceptual analysis methods, I propose a cursory account of corpus-based conceptual analysis. Then I formulate the question of similarity, and argue for an account that is functionalist in Millikan's (1984) sense. In this process, I propose a new account of concept that bases itself on millikanian teleosemantics in order to account for concepts' contribution in discourse. I then illustrate its fruitfulness by showing how it enables accounts of concept presence detection in textual data, both automatically and by a human judge. (shrink)